Commit f1dab347 authored by Fang Yuedong's avatar Fang Yuedong
Browse files

Merge branch 'new_sim' of https://csst-tb.bao.ac.cn/code/csst_sim/csst-simulation into new_sim

parents 3321919c 20067476
......@@ -20,6 +20,7 @@ def config_dir(config, work_dir=None, data_dir=None):
# PSF data directory
if config["psf_setting"]["psf_model"] == "Interp":
path_dict["psf_dir"] = os.path.join(path_dict["data_dir"], config["psf_setting"]["psf_dir"])
path_dict["psf_sls_dir"] = os.path.join(path_dict["data_dir"], config["psf_setting"]["psf_sls_dir"])
return path_dict
......
......@@ -21,6 +21,22 @@ def log_info(msg, logger=None):
else:
print(msg, flush=True)
def getChipSLSGratingID(chipID):
gratingID = ['','']
if chipID == 1: gratingID = ['GI2', 'GI1']
if chipID == 2: gratingID = ['GV4', 'GV3']
if chipID == 3: gratingID = ['GU2', 'GU1']
if chipID == 4: gratingID = ['GU4', 'GU3']
if chipID == 5: gratingID = ['GV2', 'GV1']
if chipID == 10: gratingID = ['GI4', 'GI3']
if chipID == 21: gratingID = ['GI6', 'GI5']
if chipID == 26: gratingID = ['GV8', 'GV7']
if chipID == 27: gratingID = ['GU6', 'GU5']
if chipID == 28: gratingID = ['GU8', 'GU7']
if chipID == 29: gratingID = ['GV6', 'GV5']
if chipID == 30: gratingID = ['GI8', 'GI7']
return gratingID
def getChipSLSConf(chipID):
confFile = ''
if chipID == 1: confFile = ['CSST_GI2.conf', 'CSST_GI1.conf']
......
......@@ -248,10 +248,27 @@ class Galaxy(MockObject):
xOrderSigPlus = {'A':1.3909419820029296,'B':1.4760376591236062,'C':4.035447379743442,'D':5.5684364343742825,'E':16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list), 2])
# print(hasattr(psf_model, 'bandranges'))
if hasattr(psf_model, 'bandranges'):
if psf_model.bandranges is None:
return 2, None
if len(psf_model.bandranges) != len(bandpass_list):
return 2, None
branges = psf_model.bandranges
else:
for i in range(len(bandpass_list)):
branges[i, 0] = bandpass_list[i].blue_limit * 10
branges[i, 1] = bandpass_list[i].red_limit * 10
for i in range(len(bandpass_list)):
bandpass = bandpass_list[i]
# bandpass = bandpass_list[i]
brange = branges[i]
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
disk = galsim.Sersic(n=self.disk_sersic_idx, half_light_radius=self.hlr_disk, flux=1.0, gsparams=gsp)
disk_shape = galsim.Shear(g1=self.e1_disk, g2=self.e2_disk)
disk = disk.shear(disk_shape)
......@@ -272,14 +289,14 @@ class Galaxy(MockObject):
g2 += fd_shear.g2
gal_shear = galsim.Shear(g1=g1, g2=g2)
gal = gal.shear(gal_shear)
gal = galsim.Convolve(psf, gal)
# gal = galsim.Convolve(psf, gal)
if not big_galaxy: # Not apply PSF for very big galaxy
gal = galsim.Convolve(psf, gal)
# if fd_shear is not None:
# gal = gal.shear(fd_shear)
# if not big_galaxy: # Not apply PSF for very big galaxy
# gal = galsim.Convolve(psf, gal)
# # if fd_shear is not None:
# # gal = gal.shear(fd_shear)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset,method = 'real_space')
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
......@@ -301,12 +318,16 @@ class Galaxy(MockObject):
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local=[xcenter_p1, ycenter_p1],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
subImg_p2 = starImg.array[:, subSlitPos+1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
......@@ -318,12 +339,16 @@ class Galaxy(MockObject):
sdp_p2 = SpecDisperser(orig_img=star_p2, xcenter=xcenter_p2,
ycenter=ycenter_p2, origin=origin_p2,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp_p1
del sdp_p2
......@@ -331,25 +356,33 @@ class Galaxy(MockObject):
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
elif grating_split_pos_chip>=gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
# print(self.y_nominal, starImg.center.y, starImg.ymin)
del psf
# del psf
return 1, pos_shear
def getGSObj(self, psf, g1=0, g2=0, flux=None, filt=None, tel=None, exptime=150.):
......
......@@ -4,7 +4,7 @@ import astropy.constants as cons
from astropy import wcs
from astropy.table import Table
from ObservationSim.MockObject._util import magToFlux, VC_A, convolveGaussXorders
from ObservationSim.MockObject._util import magToFlux, VC_A, convolveGaussXorders, convolveImg
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, \
getABMAG
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
......@@ -223,9 +223,69 @@ class MockObject(object):
del stamp
del spec_orders
def addSLStoChipImageWithPSF(self, sdp=None, chip=None, pos_img_local = [1,1], psf_model=None, bandNo = 1, grating_split_pos=3685, local_wcs=None, pos_img=None):
spec_orders = sdp.compute_spec_orders()
for k, v in spec_orders.items():
img_s = v[0]
# print(bandNo,k)
try:
psf, pos_shear = psf_model.get_PSF(chip, pos_img_local = pos_img_local, bandNo = bandNo, galsimGSObject=True, g_order = k, grating_split_pos=grating_split_pos)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
psf_img = psf.drawImage(nx=100, ny=100, wcs = local_wcs)
psf_img_m = psf_img.array
#########################################################
# DEBUG
#########################################################
# ids_p = psf_img_m < 0
# psf_img_m[ids_p] = 0
# from astropy.io import fits
# fits.writeto(str(bandNo) + '_' + str(k) + '_psf.fits', psf_img_m)
# print("DEBUG: orig_off is", orig_off)
nan_ids = np.isnan(img_s)
if img_s[nan_ids].shape[0] > 0:
img_s[nan_ids] = 0
print("DEBUG: specImg nan num is", img_s[nan_ids].shape[0])
#########################################################
img_s, orig_off = convolveImg(img_s, psf_img_m)
origin_order_x = v[1] - orig_off[0]
origin_order_y = v[2] - orig_off[1]
specImg = galsim.ImageF(img_s)
# photons = galsim.PhotonArray.makeFromImage(specImg)
# photons.x += origin_order_x
# photons.y += origin_order_y
# xlen_imf = int(specImg.xmax - specImg.xmin + 1)
# ylen_imf = int(specImg.ymax - specImg.ymin + 1)
# stamp = galsim.ImageF(xlen_imf, ylen_imf)
# stamp.wcs = local_wcs
# stamp.setOrigin(origin_order_x, origin_order_y)
specImg.wcs = local_wcs
specImg.setOrigin(origin_order_x, origin_order_y)
bounds = specImg.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() == 0:
continue
chip.img.setOrigin(0, 0)
chip.img[bounds] = chip.img[bounds] + specImg[bounds]
# stamp[bounds] = chip.img[bounds]
# # chip.sensor.accumulate(photons, stamp)
# chip.img[bounds] = stamp[bounds]
chip.img.setOrigin(chip.bound.xmin, chip.bound.ymin)
# del stamp
del spec_orders
return pos_shear
def drawObj_slitless(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., normFilter=None, grating_split_pos=3685, fd_shear=None):
if normFilter is not None:
norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001
sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed,
......@@ -263,22 +323,38 @@ class MockObject(object):
xOrderSigPlus = {'A': 1.3909419820029296, 'B': 1.4760376591236062, 'C': 4.035447379743442,
'D': 5.5684364343742825, 'E': 16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list),2])
if hasattr(psf_model,'bandranges'):
if psf_model.bandranges is None:
return 2, None
if len(psf_model.bandranges) != len(bandpass_list):
return 2, None
branges = psf_model.bandranges
else:
for i in range(len(bandpass_list)):
branges[i, 0] = bandpass_list[i].blue_limit * 10
branges[i, 1] = bandpass_list[i].red_limit * 10
for i in range(len(bandpass_list)):
bandpass = bandpass_list[i]
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass,
folding_threshold=folding_threshold)
# bandpass = bandpass_list[i]
brange = branges[i]
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass,
# folding_threshold=folding_threshold)
star = galsim.DeltaFunction(gsparams=gsp)
star = star.withFlux(tel.pupil_area * exptime)
star = galsim.Convolve(psf, star)
psf_tmp = galsim.Gaussian(sigma=0.002)
star = galsim.Convolve(psf_tmp, star)
starImg = star.drawImage(nx=100, ny=100, wcs=chip_wcs_local, offset=offset)
starImg = star.drawImage(nx=60, ny=60, wcs=chip_wcs_local, offset=offset)
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0,0)
gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] - 1, origin_star[1] + starImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
## part img disperse
......@@ -293,12 +369,15 @@ class MockObject(object):
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local = [xcenter_p1,ycenter_p1],
psf_model=psf_model, bandNo = i+1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
subImg_p2 = starImg.array[:, subSlitPos + 1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
......@@ -310,12 +389,15 @@ class MockObject(object):
sdp_p2 = SpecDisperser(orig_img=star_p2, xcenter=xcenter_p2,
ycenter=ycenter_p2, origin=origin_p2,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp_p1
del sdp_p2
......@@ -323,23 +405,29 @@ class MockObject(object):
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
del psf
# del psf
return 1, pos_shear
def SNRestimate(self, img_obj, flux, noise_level=0.0, seed=31415):
......
......@@ -155,7 +155,7 @@ class SpecDisperser(object):
sensitivity_beam = ysens
len_spec_x = len(dx)
len_spec_y = int(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0]) + 1)
len_spec_y = int(abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)
beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)
modelf = zeros(product(beam_sh), dtype=float)
......
......@@ -571,4 +571,14 @@ def convolveGaussXorders(img=None, sigma = 1):
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
return convImg, offset
def convolveImg(img=None, psf = None):
from astropy.modeling.models import Gaussian2D
from scipy import signal
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
offset_x = int(psf.shape[1]/2. + 0.5) - 1
offset_y = int(psf.shape[0]/2. + 0.5) - 1
offset = [offset_x,offset_y]
return convImg, offset
......@@ -15,7 +15,7 @@ from ObservationSim.Config.Header import generatePrimaryHeader, generateExtensio
from ObservationSim.Instrument import Telescope, Filter, FilterParam, FocalPlane, Chip
from ObservationSim.Instrument.Chip import Effects
from ObservationSim.Straylight import calculateSkyMap_split_g
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
from ObservationSim._util import get_shear_field, makeSubDir_PointingList
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
......@@ -63,7 +63,10 @@ class Observation(object):
if self.config["psf_setting"]["psf_model"] == "Gauss":
psf_model = PSFGauss(chip=chip, psfRa=self.config["psf_setting"]["psf_rcont"])
elif self.config["psf_setting"]["psf_model"] == "Interp":
psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples, PSF_data_file=self.path_dict["psf_dir"])
if chip.survey_type == "spectroscopic":
psf_model = PSFInterpSLS(chip, filt,PSF_data_prefix=self.path_dict["psf_sls_dir"])
else:
psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples, PSF_data_file=self.path_dict["psf_dir"])
else:
chip_output.Log_error("unrecognized PSF model type!!", flush=True)
......@@ -198,6 +201,10 @@ class Observation(object):
obj = self.cat.objs[j]
# (DEBUG)
# if obj.getMagFilter(filt)>20:
# continue
# load and convert SED; also caculate object's magnitude in all CSST bands
try:
sed_data = self.cat.load_sed(obj)
......
This diff is collapsed.
......@@ -2,4 +2,5 @@ from .PSFModel import PSFModel
from .PSFGauss import PSFGauss
# from .PSFInterp.PSFInterp import PSFInterp
from .PSFInterp import PSFInterp
from .PSFInterpSLS import PSFInterpSLS
from .FieldDistortion import FieldDistortion
\ No newline at end of file
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
<<<<<<< HEAD
work_dir: "/share/home/zhangxin/CSST_SIM/CSST_new_sim/csst-simulation/"
=======
work_dir: "/share/home/fangyuedong/new_sim/workplace/"
# work_dir: "/share/C6_new_sim_2sq"
>>>>>>> new_sim
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "C6_new_sim_2sq_run1"
project_cycle: 6
run_counter: 1
# Whether to use MPI
run_option:
use_mpi: NO
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_C6_20221212"
star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
galaxy_cat: "cat2CSSTSim_bundle/"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
# Only simulate stars?
star_only: YES
# Only simulate galaxies?
galaxy_only: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
survey_type: "Spectroscopic"
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [10]
# Whether to enable astrometric modeling
enable_astrometric_model: True
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
# mag_sat_margin: -2.5
mag_sat_margin: -15.
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
psf_sls_dir: "/share/simudata/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: YES # Whether to add field distortions
add_back: YES # Whether to add sky background
add_dark: YES # Whether to add dark noise
add_readout: YES # Whether to add read-out (Gaussian) noise
add_bias: YES # Whether to add bias-level to images
bias_16channel: YES # Whether to add different biases for 16 channels
gain_16channel: YES # Whether to make different gains for 16 channels
shutter_effect: YES # Whether to add shutter effect
flat_fielding: YES # Whether to add flat-fielding effect
prnu_effect: YES # Whether to add PRNU effect
non_linear: YES # Whether to add non-linearity
cosmic_ray: YES # Whether to add cosmic-ray
cray_differ: YES # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: YES # Whether to simulate CTE trails
saturbloom: YES # Whether to simulate Saturation & Blooming
add_badcolumns: YES # Whether to add bad columns
add_hotpixels: YES # Whether to add hot pixels
add_deadpixels: YES # Whether to add dead(dark) pixels
bright_fatter: YES # Whether to simulate Brighter-Fatter (also diffusion) effect
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
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